[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116841-en":3,"doc-seo-116841-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116841,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Experimental Evaluation of Quantum Machine Learning Algorithms","Machine learning and quantum computing are combined to study how quantum machine learning algorithms perform in practice. The work addresses gaps left by prior studies that rely on varied datasets and different evaluation setups, making systematic comparison difficult. The paper experiments with kernel-based quantum support vector machines and quantum neural networks using five datasets and multiple quantum feature maps, comparing results on both a quantum simulator and a real quantum computer, plus classical baselines. Findings show QSVMs average 3–4% higher accuracy than classical methods, and QNNs further improve performance by up to 5% over QSVMs.","Received 12 December 2022, accepted 26 December 2022, date of publication 12 January 2023, date of current version 20 January 2023. Digital Object Identifier 10.1109/ACCESS.2023.3236409  \nExperimental Evaluation of Quantum Machine Learning Algorithms  \nRICARDO DANIEL MONTEIRO SIMÕES1, PATRICK HUBER 1, NICOLA MEIER 1, NIKITA SMAILOV1, RUDOLF M. FÜCHSLIN1,2, AND KURT STOCKINGER1  \n1 School of Engineering, ZHAW Zurich University of Applied Sciences, 8401 Winterthur, Switzerland  \n2European Centre for Living Technology, 30123 Venice, Italy Corresponding author: Kurt Stockinger ([Kurt.Stockinger@zhaw.ch](Kurt.Stockinger@zhaw.ch))  \nABSTRACT Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications. Recently there have also been many theoretical contributions of quantum machine learning algorithms with experiments performed on quantum simulators. However, most questions concerning the potential of machine learning on quantum computers are still unanswered such as How well do current quantum machine learning algorithms work in practice? How do they compare with classical approaches? Moreover, most experiments use different datasets and hence it is currently not possible to systematically compare different approaches. In this paper we analyze how quantum machine learning can be used for solving small, yet practical problems. In particular, we perform an experimental analysis of kernel-based quantum support vector machines and quantum neural networks. We evaluate these algorithm on 5 different datasets using different combinations of quantum feature maps. Our experimental results show that quantum support vector machines outperform their classical counterparts on average by 3 to 4% in accuracy both on a quantum simulator as well as on a real quantum computer. Moreover, quantum neural networks executed on a quantum computer further outperform quantum support vector machines on average by up to 5% and classical neural networks by 7% .  \nINDEX TERMS Machine learning, quantum computing, experimental evaluation.  \nI. INTRODUCTION  \nHardly any other field of research in computer science has made such rapid progress in recent years as machine learning. It is used successfully in various areas both in research and in industry [18] . However, there are also limits and unsolved problems in practical applications due to the enormous computing resource requirements of large machine learning algorithms such as transformer-based language models [29] . Moreover, machine learning methods are often complex and based on large amounts of data. Therefore, depending on the task, the algorithms can become extremely computationally intensive.  \nA novel type of computer hardware, quantum computers, promises considerable speed-up so that these algorithms are  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Li He .  \nuseful for a broad class of users [5], [34] . Moreover, companies such as IBM and Amazon already provide public access to quantum computers via Python interfaces [1], [11] . This allows active research in quantum computing also for small to medium-sized research institutions or companies that do not have the computing resources of large corporations.  \nThe field of quantum machine learning has gained considerable attention in the last years [4], [7], [10], [14] . However, it is still relatively unclear what kind of problems can be solved practically today and which ones remain only of theoretical nature.  \nIn this paper we will perform an experimental evaluation of quantum support vector machines (QSVM) as well as quantum neural networks (QNN) and compare them against their classical counterparts. In our first set of experiments we will evaluate kernel-based SVMs [14] . Classical kernel-based SVMs have been studied well and have been widely applied.  \nVOLU","cbCaihgCjaaKHzAr","https://ap.wps.com/l/cbCaihgCjaaKHzAr","pdf",2175518,1,12,"English","en",105,"# Introduction\n## Scope of experimental evaluation\n## Contributions and key findings","[{\"question\":\"Which quantum machine learning methods are experimentally evaluated?\",\"answer\":\"The study evaluates kernel-based quantum support vector machines (QSVMs) and quantum neural networks (QNNs). It compares both against corresponding classical approaches.\"},{\"question\":\"How are the quantum algorithms tested and compared?\",\"answer\":\"Experiments use five different datasets and evaluate multiple quantum feature maps and circuit implementations. Performance is measured on a quantum simulator and on a real quantum computer, alongside classical baselines.\"},{\"question\":\"What accuracy improvements are reported compared with classical methods?\",\"answer\":\"QSVMs outperform classical kernel-based SVMs on average by about 3–4% in accuracy on both simulation and a real quantum computer. QNNs executed on a quantum computer further outperform QSVMs by up to 5% and classical neural networks by up to 7%.\"}]","Experimental Evaluation of Quantum Machine Learning Algorithms | PDF",1785672033,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"experimental-evaluation-of-quantum-machine-learning-algorithms","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/experimental-evaluation-of-quantum-machine-learning-algorithms/116841/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which quantum machine learning methods are experimentally evaluated?","Question",{"text":76,"@type":77},"The study evaluates kernel-based quantum support vector machines (QSVMs) and quantum neural networks (QNNs). It compares both against corresponding classical approaches.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are the quantum algorithms tested and compared?",{"text":81,"@type":77},"Experiments use five different datasets and evaluate multiple quantum feature maps and circuit implementations. Performance is measured on a quantum simulator and on a real quantum computer, alongside classical baselines.",{"name":83,"@type":74,"acceptedAnswer":84},"What accuracy improvements are reported compared with classical methods?",{"text":85,"@type":77},"QSVMs outperform classical kernel-based SVMs on average by about 3–4% in accuracy on both simulation and a real quantum computer. QNNs executed on a quantum computer further outperform QSVMs by up to 5% and classical neural networks by up to 7%.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]